Abstract:
The discrete Wavelet transform is capable of providing the time and frequency information simultaneously, hence giving a time-frequency representation of the signal for which wavelet are discretely sampled. Some of the application of DWT are image processing, data compression, biomedical signal
processing. In biomedical signal processing, DWT becomes a powerful technique. There are two architectures for DWT, one is convolution based and the other is lifting based. The lifting scheme provides many advantages, such as in-place implementation, fewer arithmetic operations and easy management of boundary extension. In lifting plan for Daubechies 9/7 channel, each lifting step comprises of one predict and one update step. It offers a higher quality of image restoration, higher coding efficiency. The VHDL code of proposed DWT/IDWT architecture is synthesized using Xilinx ISE 14.4 for FPGA Artix-7 family and simulated using Xilinx Isim simulator. The proposed VLSI architecture of Generic DWT and IDWT is used for denoising in filtered output DNA
sequence for Exon region identification in Eukaryotic genes. The advantage of generic DWT/IDWT is that the design is input independent. In this design, the DWT module doesn't depend on number of input and reduces the logic unit in DWT which in turn reducing the chip area.
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